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The 3D components of today's user interfaces are still underdeveloped. Direct interaction with 3D objects has been limited thus far to gestural picking, manipulation with linear transformations, and simple camera motion. Further, there are no toolkits for building 3D user interfaces. We present a system which allows experimentation with 3D widgets, encapsulated3D geometry and behavior. Our widgets are first-class objects in the same 3D environment used to develop the application. This integration of widgets and application objects provides a higher bandwidth between interface and application than exists in more traditional UI toolkit-based interfaces. We hope to allow user-interface designers to build highly interactive 3D environments more easily than is possible with today's tools.

3D computer graphics is becoming more and more popular due to the increased availability of 3D hardware and software on all classes of computers. However, despite this growing popularity and the existence of a number of successful 3D graphics applications, particularly in CAD, CAE, and medical and scientific visualization, the field is still very immature, There are no widely accepted standards for hardware or software platforms; learning to implement or use 3D graphics software is still extremely laborious; and the most effective ways for humans to interact with synthetic 3D environments are still not clear.

This article provides a snapshot of immersive virtual reality (IVR) use for scientific visualization, in the context of the evolution of computing in general and of user interfaces in particular. The main thesis of this article is that IVR has great potential for dealing with the serious problem of exponentially growing scientific datasets. Our ability to produce large datasets both through numerical simulation and through data acquisition via sensors is outrunning our ability to make sense of those datasets. While our idea of ''large'' datasets used to be measured in hundreds of gigabytes, based at least in part on what we could easily store, manipulate, and display in real time, today's science and engineering are producing terabytes and soon even petabytes, both from observation via sensors and as output from numerical simulation. Clearly, visualization by itself will not solve the problem of understanding truly large datasets that would overwhelm both display capacity and the human visual system. We advocate a human-computer partnership that draws on the strengths of each partner, with algorithmic culling and feature-detection used to identify the small fraction of the data that should be visually examined in detail by the human. Our hope is that IVR will be a potent tool to let humans ''see'' patterns, trends, and anomalies in their data well beyond what they can do with conventional 3D desktop displays. r